Blended learning for accredited life support courses – A systematic review
Bibliographic record
Abstract
Aim: To evaluate the effectiveness on educational and resource outcomes of blended compared to non-blended learning approaches for participants undertaking accredited life support courses. Methods: This review was conducted in adherence with PRISMA standards. We searched EMBASE.com (including all journals listed in Medline), CINAHL and Cochrane from 1 January 2000 to 6 August 2021. Randomised and non-randomised studies were eligible for inclusion. Study screening, data extraction, risk of bias assessment (using RoB2 and ROBINS-I tools), and certainty of evidence evaluation (using GRADE) were all independently performed in duplicate. The systematic review was registered with PROSPERO (CRD42022274392). Results: From 2,420 studies, we included data from 23 studies covering fourteen basic life support (BLS) with 2,745 participants, eight advanced cardiac life support (ALS) with 33,579 participants, and one Advanced Trauma Life Support (ATLS) with 92 participants. Blended learning is at least as effective as non-blended learning for participant satisfaction, knowledge, skills, and attitudes. There is potential for cost reduction and eventual net profit in using blended learning despite high set up costs. The certainty of evidence was very low due to a high risk of bias and inconsistency. Heterogeneity across studies precluded any meta-analysis. Conclusion: Blended learning is at least as effective as non-blended learning for accredited BLS, ALS, and ATLS courses. Blended learning is associated with significant long term cost savings and thus provides a more efficient method of teaching. Further research is needed to investigate specific delivery methods and the effect of blended learning on other accredited life support courses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".